Delegate your repetitive tasks

Every job involves some amount of repetitive work. The account executive who needs to enter information into their CRM, or the HR executive who needs to answer the same question about paid time off.

If these tasks are repetitive, why hasn't software delivered on the promise of automation? Usually, there are two reasons: first, the task may be ever so slightly different when you choose to do it. Second, the effort of automating the task may have exceeded the value of doing so.

Thinking on the fly

Large language models (LLMs) allow us to address both of these issues, unlocking automation like never before. It is now possible to build automations that are fluid. Consider the sales executive who needs to keep their CRM up to date. The information required to update the CRM lives in calls, emails and any notes they've taken. If they need to format this information for the CRM, they may as well just do it via the Salesforce (or whatever CRM they use) user interface.

On the other hand, if the update happened automatically by plugging into these resources, it would save the Account Executive precious time. Time that can be spent following up with leads and closing more deals. Large language models make this possible because they provide intelligence on the fly. The LLM can summarise the content and format it for the CRM. In the best case scenario, the AE needs to do nothing because information moves seamlessly into the CRM.

Human in the loop

In some cases, we will find that AI needs to work in partnership with a human. The best example of this is the financial operations function. Every business needs to receive invoices, check them and pay vendors. This is typically handled by an accounts payable function.

Today, most businesses have software that helps with this but the automation is incomplete. This is because every business has it's unique process for handling invoices, and because every invoice looks different. Traditional automation tools like robotic process automation have low success rates because of this variance in the process.

LLMs address this shortcoming because they allow us to process information that may not always follow the same structure, like invoices. They significantly improve accuracy and reduce fraud. Even with LLMs, you require a human in the loop to avoid any chance of a mistake. The difference though is that it allows your team to handle more invoices whilst simultaneously improving reliability.

This human, AI collaboration also allows your team to work on the most pressing issues. For example, an invoice that looks fraudulent or resolving an issue that may have sent the funds to the wrong bank account.

Employees managing digital workers

At Kili, we believe AI will enable every employee to 10x their output. We believe they will do so by managing digital workers that complete repetitive tasks for them. Instead of spending time getting context from different systems, employees will be able to spend time on what decision to make.

We believe in a world where AI quietly does work for your team in the background, and only reaches out to them when they need to make a decision. We're excited for this future and are building for it.

Join our closed beta if you're interested in leveraging this capability for your business.